Prediction in the joint modeling of mixed types of multivariate longitudinal outcomes and a time-to-event outcome
Year: 2012
Type: article
Abstract: A common goal of longitudinal studies is to relate a set of repeated observations to a time-to-event endpoint. One example of such a design is in the area of a late-life depression research where repeated measurement of cognitive and functional outcomes can contribute to one's ability to predict whether or not an individual will have a major depressive episode over a period of time. This research proposes a novel model for the relationship between multivariate longitudinal measurements and a time-to-event outcome. The goal of this model is to improve prediction for the time-to-event outcome by considering all longitudinal measurements simultaneously. In this dissertation, we investigate a joint modeling approach for mixed types of multivariate longitudinal outcomes and a time-to-event outcome using a Bayesian paradigm. For the longitudinal model of continuous and binary outcomes, we formulate multivariate generalized linear mixed models with two types of random effects structures: shared random effects and correlated random effects. For the joint model, the longitudinal outcomes and the time-to-event outcome are assumed to be independent conditional on available covariates and the shared parameters, which are associated with the random effects of the longitudinal outcome processes. A Bayesian method using Markov chain Monte Carlo (MCMC) computed in OpenBUGS is implemented for parameter estimation. We illustrate the prediction of future event probabilities within a fixed time interval for patients based on our joint model, utilizing baseline data, post-baseline longitudinal measurements, and the time-to-event outcome. Prediction of event or mortality probabilities allows one to intervene clinically when appropriate. Hence, such methods provide a useful public health tool at both the individual and the population levels. The proposed joint model is applied to data sets on the maintenance therapies in a late-life depression study and the mortality in idiopathic pulmonary fibrosis. The performance of the method is also evaluated in extensive simulation studies.
Author: Jiin Choi
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W89010822